Dynamic obstacle avoidance for car-like mobile robots based on neurodynamic optimization with control barrier functions
This paper studies the problem of dynamic obstacle avoidance for car-like mobile robots with physical limitations, including bounded steering angles and input saturation. By designing control barrier functions (CBFs), a novel collision-free module is proposed, which applies minimal adjustments to th...
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| Published in | Neurocomputing (Amsterdam) Vol. 654; p. 131252 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
14.11.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 |
| DOI | 10.1016/j.neucom.2025.131252 |
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| Abstract | This paper studies the problem of dynamic obstacle avoidance for car-like mobile robots with physical limitations, including bounded steering angles and input saturation. By designing control barrier functions (CBFs), a novel collision-free module is proposed, which applies minimal adjustments to the velocity commands of a given controller when necessary to ensure dynamic obstacle avoidance motion under the physical limitations. Specifically, the module is implemented as a quadratic program that is both subject to constraints derived from the CBFs and solved in real-time through neurodynamic optimization. Compared with the existing results, the proposed scheme ensures real-time generation of physically realizable safe commands and enhances steering maneuverability for obstacle avoidance. Finally, the safety of the designed method is theoretically proven, and its effectiveness in dynamic obstacle avoidance is verified via numerical simulations. |
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| AbstractList | This paper studies the problem of dynamic obstacle avoidance for car-like mobile robots with physical limitations, including bounded steering angles and input saturation. By designing control barrier functions (CBFs), a novel collision-free module is proposed, which applies minimal adjustments to the velocity commands of a given controller when necessary to ensure dynamic obstacle avoidance motion under the physical limitations. Specifically, the module is implemented as a quadratic program that is both subject to constraints derived from the CBFs and solved in real-time through neurodynamic optimization. Compared with the existing results, the proposed scheme ensures real-time generation of physically realizable safe commands and enhances steering maneuverability for obstacle avoidance. Finally, the safety of the designed method is theoretically proven, and its effectiveness in dynamic obstacle avoidance is verified via numerical simulations. |
| ArticleNumber | 131252 |
| Author | Yang, Guang-Hong Zhang, Zheng |
| Author_xml | – sequence: 1 givenname: Zheng surname: Zhang fullname: Zhang, Zheng email: Zhangzheng2023@126.com organization: College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China – sequence: 2 givenname: Guang-Hong surname: Yang fullname: Yang, Guang-Hong email: yangguanghong@ise.neu.edu.cn organization: College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China |
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| Keywords | Control barrier functions Car-like mobile robots Dynamic obstacle avoidance Neurodynamic optimization |
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| SubjectTerms | Car-like mobile robots Control barrier functions Dynamic obstacle avoidance Neurodynamic optimization |
| Title | Dynamic obstacle avoidance for car-like mobile robots based on neurodynamic optimization with control barrier functions |
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